Scene Image is Non-Mutually Exclusive - A Fuzzy Qualitative Scene Understanding
This addresses ambiguity in scene understanding for computer vision, offering a more nuanced approach than conventional binary classification, though it is incremental in refining existing methods.
The paper tackles the problem of scene understanding by challenging the assumption that scene images are mutually exclusive, proposing the Fuzzy Qualitative Rank Classifier (FQRC) to provide ranking interpretations instead of binary decisions, with evaluations on large and challenging public datasets showing its effectiveness.
Ambiguity or uncertainty is a pervasive element of many real world decision making processes. Variation in decisions is a norm in this situation when the same problem is posed to different subjects. Psychological and metaphysical research had proven that decision making by human is subjective. It is influenced by many factors such as experience, age, background, etc. Scene understanding is one of the computer vision problems that fall into this category. Conventional methods relax this problem by assuming scene images are mutually exclusive; and therefore, focus on developing different approaches to perform the binary classification tasks. In this paper, we show that scene images are non-mutually exclusive, and propose the Fuzzy Qualitative Rank Classifier (FQRC) to tackle the aforementioned problems. The proposed FQRC provides a ranking interpretation instead of binary decision. Evaluations in term of qualitative and quantitative using large numbers and challenging public scene datasets have shown the effectiveness of our proposed method in modeling the non-mutually exclusive scene images.